Last updated on November 10th, 2017
There are so many ways to normalize vectors… A common preprocessing step in machine learning is to normalize a vector before passing the vector into some machine learning algorithm e.g., before training a support vector machine (SVM).
One way to normalize the vector is to apply l2-normalization
to scale the vector to have a unit norm
. “Unit norm” essentially means that if we squared each element in the vector, and summed them, it would equal 1
.
(note this normalization is also often referred to as, unit norm
or a vector of length 1
or a unit vector
).
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